The CP, PPoS and PoS along with power are often used statistical tools to quantify the chance of success (either “trial success” or “clinical success”). Of these, power and PoS are calculated at the beginning of the trial, whereas CP and PPoS are determined after observing the interim results. All these measures attempt to determine pr(success) (i.e., chance of success) based on the prior belief of effect size at the beginning of the trial, or the observed value of effect size in the first part of the trial, or the combination of both. At the beginning of a trial, pr(success) is determined solely based on the prior belief. On the other hand, calculation of pr(success) at the interim analysis has to take following things into consideration: (a) available interim results, (b) limited uncertainty in the trial data arising from the post-interim part, and (c) the decision to use or not the prior information. Further, power and CP are frequentist tools, whereas PoS and PPoS follow the Bayesian paradigm. The difference of Bayesian approach over the frequentist approach is the way available knowledge on effect size is summarized: Bayesian measures summarize this knowledge as distribution of effect whereas frequentist measure makes the best guess about the effect size as a single value, and thereby frequentist measures may not be a good indicator of pr(success) [1]}, [2]}, [3]}, [4]}. For this reason, PoS is also viewed as average `power' over the prior distribution of \(\theta \)  [3]}, [4]} whereas PPoS is the average CP over the predictive distribution of \(\theta \) . Further, in the Bayesian approach, both prior knowledge and available interim results can be used together in assessing pr(success); however, all available knowledge must be summarized into a single value in the frequentist approach.
